Saudi Cultural Missions Theses & Dissertations

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    Exploring the Impact of Talent Management Strategies on AI Adoption in Saudi Arabia’s Emerging Tech Startups: The Mediating Role of Knowledge Sharing
    (Saudi Digital Library, 2025) Abuhaimed, Mohammad Saad; Abdoulrahman Aljounaidi Mhd Ramez
    Saudi Arabia's Vision 2030 emphasizes AI-driven digital transformation, yet tech startups struggle to scale AI beyond pilots. Purpose: This study examines how talent management (TM) strategies—attracting-selecting (AST), developing (DT), empowering (ET), retaining (RT), and career succession (CS)—shape AI adoption, and whether knowledge sharing (KS) mediates this relationship. Method: Using probability-based systematic random sampling of employees (n=337, N=2,308) across Saudi AI-adopting startups, the model was analyzed with PLS-SEM (SmartPLS 4). Findings: AST, DT, and ET positively affect AI adoption; RT shows no effect; CS exhibits a negative effect. KS partially mediates AST, DT, ET, and CS effects, indicating TM practices influence adoption primarily through knowledge institutionalization. Implications—Industrial: Startup leaders should integrate KS infrastructures with TM initiatives. Recommended practices: (1) cross-functional AI taskforces with rotating membership; (2) peer-learning sessions where early adopters mentor colleagues; (3) searchable repositories (wikis, Confluence) documenting implementation lessons and troubleshooting guides; (4) succession systems prioritizing collaborative knowledge transfer (mentoring, communities of practice) to prevent silos. Empirical evidence shows succession planning without KS scaffolding correlates negatively with adoption (β = -0.182, p < .01), highlighting knowledge-hoarding risks. Academic: The study extends technology-acceptance theory by integrating human-capital antecedents and positioning KS as the pivotal mediating mechanism in resource-constrained startups. Testing 16 structural paths across five TM dimensions addresses three gaps: (1) mechanistic under-specification, (2) construct aggregation bias, and (3) non-Western context neglect. The mediation framework—validated through bootstrapped indirect effects—provides a replicable blueprint for future research examining causality, moderators (industry velocity, founder literacy), and boundary conditions.
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    The Influence of AI-Literate Leadership on AI Adoption in Healthcare Organizations
    (Saudi Digital Library, 2025) Almuhrij, Abdullateef; Chipidza, Wallace
    The adoption of Artificial Intelligence (AI) in healthcare has the potential to revolutionize patient care, optimize operations, and advance diagnostic precision. However, successful adoption depends on AI-literate leadership capable of addressing ethical, technical, and organizational challenges. This study examined how healthcare leaders in Saudi Arabia perceived and engaged with the role of AI in healthcare, addressing a critical gap in understanding leadership competencies within the framework of Saudi Arabia’s Vision 2030. Using a qualitative case study design, the research explores leadership perceptions and their influence on AI adoption through semi-structured interviews with 22 healthcare leaders across diverse institutions. Thematic analysis is employed to identify patterns in the data, revealing how leadership demographics, organizational context, and varying levels of AI literacy influenced adoption strategies, perceived barriers, and readiness for digital transformation. The findings contribute theoretically to the understanding of leadership in the context of digital transformation and provide practical strategies to support AI adoption in healthcare organizations. By highlighting the role of AI-literate leadership in navigating challenges such as workforce resistance, ethical concerns, and infrastructural limitations, the study offers insights to guide sustainable and strategic AI adoption within Saudi Arabia’s evolving healthcare system.
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    Trust and Adoption of AI-Powered Cybersecurity in Cloud Computing
    (Saudi Digital Library, 2025) Algarni, Moneer Mohammed; Baihe, Ma
    This research investigates the trust and adoption of AI-powered cybersecurity solutions in cloud computing environments. As organizations increasingly rely on cloud services, traditional security approaches fall short in addressing evolving cyber threats. AI-driven tools offer advanced threat detection, anomaly identification, and automated response capabilities. However, concerns about trust, transparency, technical complexity, and data privacy continue to hinder widespread adoption. This study employs a mixed-methods approach, combining surveys and case studies, to explore the key factors influencing trust in AI systems and the barriers to their implementation. The findings highlight the importance of explainable AI, third-party audits, and staff training in building confidence. The research concludes with practical recommendations to help organizations integrate AI into cloud security frameworks effectively.
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